Key Concepts

Understand template's structure

This section explains the core concepts and architecture of this template.

Code Structure

The template's code is organized into three main components:

📁
├── 📁 pipelines/             # Data pipelines:
│   ├── 📁 ingest/                      # Data ingestion layer
│   ├── 📁 transform/                   # Data transformation layer
│   └── 📁 orchestrate/                 # Workflow orchestration layer
│
├── 📁 base/                  # Cloud infrastructure (VPC, roles, users, compute cluster, etc.)
│
└── 📁 live/                  # Environment-specific deployment configuration

Each component is documented separately here:

pipelines/base/aws/live/

Data Flow

Source data is ingested into Apache Iceberg landing tables: code in pipelines/ingest/<source_name>-*/
Data transformations are applied to create staging tables using SQL engine (Amazon Athena): code in pipelines/transform/

Data Pipeline Architecture

Our data platform follows a layered architecture:

1. Data Ingestion Layer

For each source, the ingestion layer is structured as follows:

📁 pipelines/
├── 📁 ingest/
│   ├── 📁 <source>-ingestion/      # Core ingestion logic
│   │
│   └── 📁 <source>-schema/         # Iceberg Table schema definitions
│       └── <table_name>.py
│       └── ...
│
└── <source>_*.tf                   # Infrastructure definition (serverless functions, containers, etc.)

Each source has:

A folder pipelines/ingest/<source>-ingestion/ containing the core ingestion logic packaged in a container
Infrastructure as Code files in pipelines/*tf for deploying this ingestion container (as serverless functions (AWS lambda) or container tasks (Amazon ECS))
A folder for the management of the landing tables (<source>-schema/)

More info about landing table schema evolution in Iceberg Landing Table Schema Evolution

The template comes with an example data ingestion pipeline deployed as a serverless function (lambda) using dlt; more details here:

Ingestion: dlt + lambda

2. Data Transformation Layer

The transformation layer is a dbt project that transforms the data into Iceberg staging tables using the SQL query engine Amazon Athena.

This project is located in the pipelines/transform folder:

📁 pipelines/
├── 📁 transform/                   # SQL transformation project
│   ├── 📁 models/
│   │   ├── 📁 staging/            # Raw table connections
│   │   └── 📁 marts/              # Transformations
│   │
│   ├── dbt_project.yml
│   └── Dockerfile                  # For container deployment
│
└── ecs_task_dbt.tf                 # Infrastructure definition for running dbt container

This transformation project runs on container infrastructure (Amazon ECS Fargate).

More details on how this transformation project is structured here:

Transformation: dbt

3. Workflow Orchestration Layer

The orchestration layer coordinates the execution of the ingestion and transformation layers using workflow automation.

This template proposes an example orchestration using AWS Step Functions:

📁 pipelines/
├── 📁 orchestrate/
│   └── <source>_step_function.json  # Workflow definition
│
└── <source>_step_function.tf        # Creates an orchestration workflow in AWS Step Functions

Deployment

This template is ready to be deployed.

The stack deployment is structured in 3 steps:

First, the infrastructure modules (base/ and pipelines/) are deployed using Terragrunt for infrastructure management
Then, the containers for the ingestion and transformation layers are built and pushed to the container registry
Finally, the schema evolution scripts of the Iceberg landing tables are run